{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4a5c591b-7e95-4b79-991c-02d85fe112a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "from scipy.stats import pearsonr, spearmanr\n",
    "import scipy.sparse as sparse\n",
    "from scipy.stats import bernoulli, poisson\n",
    "import analysis_utils_mine as utils\n",
    "\n",
    "import json\n",
    "import pandas as pd\n",
    "import ast\n",
    "from datetime import datetime\n",
    "import torch\n",
    "import pandas as pd\n",
    "from datetime import datetime, timedelta\n",
    "import pickle\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import xlsxwriter\n",
    "from sklearn.metrics import cohen_kappa_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "367fc3b5-1254-4fe5-a271-fe1bf3c67722",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "19626f46-f810-4ecf-88f5-5e99db78acf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dataframe_from_annotated_xlsx_file_path(path):\n",
    "    df = pd.read_excel(path,\n",
    "                       sheet_name=None,\n",
    "                       engine='openpyxl')\n",
    "    df = df['Sheet1']\n",
    "    df = df[~pd.isnull(df['Topic Name'])]\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4ce17236-234c-42b3-859f-86b410265f43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 50 entries, 1 to 1569\n",
      "Data columns (total 7 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Topic           50 non-null     object \n",
      " 1   Unnamed: 1      0 non-null      float64\n",
      " 2   Coherence       50 non-null     float64\n",
      " 3   Polarization    48 non-null     float64\n",
      " 4   Topic Name      50 non-null     object \n",
      " 5   Description     48 non-null     object \n",
      " 6   Notes/Comments  10 non-null     object \n",
      "dtypes: float64(3), object(4)\n",
      "memory usage: 3.1+ KB\n"
     ]
    }
   ],
   "source": [
    "annotator1_pre_tbip_speeches = get_dataframe_from_annotated_xlsx_file_path('venue_diff_polsci/pre_tbip_annotation_results/annotator_1/topics_for_annotation.xlsx_frazier_speech.xlsx')\n",
    "annotator1_pre_tbip_speeches.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6524b618-81bb-4f9c-bb9b-723a6d306908",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 50 entries, 1 to 1569\n",
      "Data columns (total 7 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Topic           50 non-null     object \n",
      " 1   Unnamed: 1      0 non-null      float64\n",
      " 2   Coherence       50 non-null     float64\n",
      " 3   Polarization    50 non-null     float64\n",
      " 4   Topic Name      50 non-null     object \n",
      " 5   Description     50 non-null     object \n",
      " 6   Notes/Comments  50 non-null     object \n",
      "dtypes: float64(3), object(4)\n",
      "memory usage: 3.1+ KB\n"
     ]
    }
   ],
   "source": [
    "annotator1_pre_tbip_tweets = get_dataframe_from_annotated_xlsx_file_path('venue_diff_polsci/pre_tbip_annotation_results/annotator_1/topics_for_annotation.xlsx_Frazier_tweet.xlsx')\n",
    "annotator1_pre_tbip_tweets.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "cf9e57b8-fc2d-4e44-9acc-3f5c7f6d2123",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 50 entries, 1 to 1569\n",
      "Data columns (total 7 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Topic           50 non-null     object \n",
      " 1   Unnamed: 1      0 non-null      float64\n",
      " 2   Coherence       50 non-null     float64\n",
      " 3   Polarization    42 non-null     float64\n",
      " 4   Topic Name      50 non-null     object \n",
      " 5   Description     43 non-null     object \n",
      " 6   Notes/Comments  16 non-null     object \n",
      "dtypes: float64(3), object(4)\n",
      "memory usage: 3.1+ KB\n"
     ]
    }
   ],
   "source": [
    "annotator2_pre_tbip_speeches = get_dataframe_from_annotated_xlsx_file_path('venue_diff_polsci/pre_tbip_annotation_results/annotator_2/topics_for_annotation_Hightower_speech.xlsx')\n",
    "annotator2_pre_tbip_speeches.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a17d4273-8143-4e2d-b387-5133a0069809",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 50 entries, 1 to 1569\n",
      "Data columns (total 7 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Topic           50 non-null     object \n",
      " 1   Unnamed: 1      0 non-null      float64\n",
      " 2   Coherence       50 non-null     float64\n",
      " 3   Polarization    39 non-null     float64\n",
      " 4   Topic Name      50 non-null     object \n",
      " 5   Description     38 non-null     object \n",
      " 6   Notes/Comments  22 non-null     object \n",
      "dtypes: float64(3), object(4)\n",
      "memory usage: 3.1+ KB\n"
     ]
    }
   ],
   "source": [
    "annotator2_pre_tbip_tweets = get_dataframe_from_annotated_xlsx_file_path('venue_diff_polsci/pre_tbip_annotation_results/annotator_2/topics_for_annotation_Hightower_tweets.xlsx')\n",
    "annotator2_pre_tbip_tweets.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4669012e-5f32-4b28-9a56-641a452e0a0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "discard_labels_to_one_discard_label_map = {'DISCARD': 'DISCARD',\n",
    "                                           'Discard': 'DISCARD',\n",
    "                                           'Disgard': 'DISCARD',\n",
    "                                           'discard': 'DISCARD'}\n",
    "                                           "
   ]
  },
  {
   "attachments": {
    "4141b89e-16d5-492d-a639-a79545d856b6.png": {
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"
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"
    }
   },
   "cell_type": "markdown",
   "id": "9fb8f43b-e365-4661-a7cc-82fa099e97a9",
   "metadata": {},
   "source": [
    "![image.png](attachment:4141b89e-16d5-492d-a639-a79545d856b6.png)![image.png](attachment:a57cf57f-c0da-4381-9312-368689384f69.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9901ee0-470f-4567-8b92-c70618d9b247",
   "metadata": {},
   "source": [
    "## Pre-TBIP results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14deecfa-3f11-4efe-b8df-99e454002a5b",
   "metadata": {},
   "source": [
    "_Coherence:_ Rate the topic, on a 1-3 scale, on its coherence (1 being not coherent, 3 being very coherent). Does that topic represent an easily identifiable category or a meaningful concept? The top words for the topic, along with the top documents associated with that topic help make this judgment. Broadly, a set of items can be said to be coherent if they enable human recognition of an identifiable category when viewed together. \n",
    "\n",
    "\n",
    "_Polarization_: If the topic is rated above 1 for coherence, rate the topic, on a 1-3 scale (1 being not polarized, 3 being polarized), on the expected polarization of this topic: do you expect meaningful ideological differences in the way liberals and conservatives would talk about the category or concept or issue or the stance they would hold on that issue? The top words for the topic, along with the top documents associated with that topic, as well as your personal knowledge of American politics will help make this judgment. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc51c36b-f2c5-4435-9c3b-9f86a1bec94e",
   "metadata": {},
   "source": [
    "Speeches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0672f181-98b6-4660-adbe-f26ed943b3e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1.0, 2.0, 3.0}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(annotator1_pre_tbip_speeches['Coherence'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "61308ce6-f87b-4ec8-9ec0-b79828159016",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1.0, 2.0, 3.0}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(annotator2_pre_tbip_speeches['Coherence'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6ce9ee33-d2d6-42c2-aea3-833395c23c2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{nan, 1.0, 3.0, nan}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(annotator1_pre_tbip_speeches['Polarization'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "44fa80a2-19ec-4fd6-8755-36d925659706",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{nan, 1.0, 2.0, 3.0, nan, nan, nan, nan}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(annotator2_pre_tbip_speeches['Polarization'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f7252a4f-c54e-4931-8407-65db9a5ec3c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_stats_col(df, colname):\n",
    "    n = len(df)\n",
    "    l = list(df[colname])\n",
    "    print('% of Topics rated 1 on ' + colname + ' = ' + str(round(100*(l.count(1.0)/n), 2)) + '%')\n",
    "    print('% of Topics rated 2 on ' + colname + ' = ' + str(round(100*(l.count(2.0)/n), 2)) + '%')\n",
    "    print('% of Topics rated 3 on ' + colname + ' = ' + str(round(100*(l.count(3.0)/n), 2)) + '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "22d7a324-95e1-4793-9953-fe4879d03a07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Annotator 1 - Speeches\n",
      "% of Topics rated 1 on Coherence = 4.0%\n",
      "% of Topics rated 2 on Coherence = 2.0%\n",
      "% of Topics rated 3 on Coherence = 94.0%\n"
     ]
    }
   ],
   "source": [
    "print('Annotator 1 - Speeches')\n",
    "get_stats_col(annotator1_pre_tbip_speeches, 'Coherence')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d43d97df-bce2-4182-b1fe-14929bd419eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Annotator 2 - Speeches\n",
      "% of Topics rated 1 on Coherence = 16.0%\n",
      "% of Topics rated 2 on Coherence = 6.0%\n",
      "% of Topics rated 3 on Coherence = 78.0%\n"
     ]
    }
   ],
   "source": [
    "print('Annotator 2 - Speeches')\n",
    "get_stats_col(annotator2_pre_tbip_speeches, 'Coherence')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8bb78c64-47f3-4b41-854a-059dfaed904c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15123456790123457"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohen_kappa_score(list(annotator1_pre_tbip_speeches['Coherence']),\n",
    "                  list(annotator2_pre_tbip_speeches['Coherence']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2012173d-4758-4a67-b357-fc88d26354d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Annotator 1 - Tweets\n",
      "% of Topics rated 1 on Coherence = 0.0%\n",
      "% of Topics rated 2 on Coherence = 2.0%\n",
      "% of Topics rated 3 on Coherence = 98.0%\n"
     ]
    }
   ],
   "source": [
    "print('Annotator 1 - Tweets')\n",
    "get_stats_col(annotator1_pre_tbip_tweets, 'Coherence')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a27930b0-37ca-4c2d-bd58-640de32ec6d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Annotator 2 - Tweets\n",
      "% of Topics rated 1 on Coherence = 22.0%\n",
      "% of Topics rated 2 on Coherence = 8.0%\n",
      "% of Topics rated 3 on Coherence = 70.0%\n"
     ]
    }
   ],
   "source": [
    "print('Annotator 2 - Tweets')\n",
    "get_stats_col(annotator2_pre_tbip_tweets, 'Coherence')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9de8e248-060c-4d9d-8014-306beb4a98a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03969270166453276"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohen_kappa_score(list(annotator1_pre_tbip_tweets['Coherence']),\n",
    "                  list(annotator2_pre_tbip_tweets['Coherence']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7278f01-7e5b-446a-b12d-23aacbc7eb00",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "fce1c42a-441e-43fb-bd88-78cb21f5f386",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Annotator 1 - Speeches\n",
      "% of Topics rated 1 on Polarization = 44.0%\n",
      "% of Topics rated 2 on Polarization = 0.0%\n",
      "% of Topics rated 3 on Polarization = 52.0%\n",
      "Annotator 2 - Speeches\n",
      "% of Topics rated 1 on Polarization = 46.0%\n",
      "% of Topics rated 2 on Polarization = 8.0%\n",
      "% of Topics rated 3 on Polarization = 30.0%\n",
      "Annotator 1 - Tweets\n",
      "% of Topics rated 1 on Polarization = 48.0%\n",
      "% of Topics rated 2 on Polarization = 0.0%\n",
      "% of Topics rated 3 on Polarization = 52.0%\n",
      "Annotator 2 - Tweets\n",
      "% of Topics rated 1 on Polarization = 52.0%\n",
      "% of Topics rated 2 on Polarization = 10.0%\n",
      "% of Topics rated 3 on Polarization = 16.0%\n"
     ]
    }
   ],
   "source": [
    "print('Annotator 1 - Speeches')\n",
    "get_stats_col(annotator1_pre_tbip_speeches, 'Polarization')\n",
    "print('Annotator 2 - Speeches')\n",
    "get_stats_col(annotator2_pre_tbip_speeches, 'Polarization')\n",
    "print('Annotator 1 - Tweets')\n",
    "get_stats_col(annotator1_pre_tbip_tweets, 'Polarization')\n",
    "print('Annotator 2 - Tweets')\n",
    "get_stats_col(annotator2_pre_tbip_tweets, 'Polarization')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f15b93e5-8e8e-413b-b7e7-b13be130ef88",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 50 entries, 1 to 1569\n",
      "Data columns (total 7 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Topic           50 non-null     object \n",
      " 1   Unnamed: 1      0 non-null      float64\n",
      " 2   Coherence       50 non-null     float64\n",
      " 3   Polarization    50 non-null     float64\n",
      " 4   Topic Name      50 non-null     object \n",
      " 5   Description     48 non-null     object \n",
      " 6   Notes/Comments  10 non-null     object \n",
      "dtypes: float64(3), object(4)\n",
      "memory usage: 3.1+ KB\n",
      "None\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 50 entries, 1 to 1569\n",
      "Data columns (total 7 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Topic           50 non-null     object \n",
      " 1   Unnamed: 1      0 non-null      float64\n",
      " 2   Coherence       50 non-null     float64\n",
      " 3   Polarization    50 non-null     float64\n",
      " 4   Topic Name      50 non-null     object \n",
      " 5   Description     50 non-null     object \n",
      " 6   Notes/Comments  50 non-null     object \n",
      "dtypes: float64(3), object(4)\n",
      "memory usage: 3.1+ KB\n",
      "None\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 50 entries, 1 to 1569\n",
      "Data columns (total 7 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Topic           50 non-null     object \n",
      " 1   Unnamed: 1      0 non-null      float64\n",
      " 2   Coherence       50 non-null     float64\n",
      " 3   Polarization    50 non-null     float64\n",
      " 4   Topic Name      50 non-null     object \n",
      " 5   Description     43 non-null     object \n",
      " 6   Notes/Comments  16 non-null     object \n",
      "dtypes: float64(3), object(4)\n",
      "memory usage: 3.1+ KB\n",
      "None\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 50 entries, 1 to 1569\n",
      "Data columns (total 7 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Topic           50 non-null     object \n",
      " 1   Unnamed: 1      0 non-null      float64\n",
      " 2   Coherence       50 non-null     float64\n",
      " 3   Polarization    50 non-null     float64\n",
      " 4   Topic Name      50 non-null     object \n",
      " 5   Description     38 non-null     object \n",
      " 6   Notes/Comments  22 non-null     object \n",
      "dtypes: float64(3), object(4)\n",
      "memory usage: 3.1+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "annotator1_pre_tbip_speeches['Polarization'] = annotator1_pre_tbip_speeches['Polarization'].fillna(0.0)\n",
    "print(annotator1_pre_tbip_speeches.info())\n",
    "annotator1_pre_tbip_tweets['Polarization'] = annotator1_pre_tbip_tweets['Polarization'].fillna(0.0)\n",
    "print(annotator1_pre_tbip_tweets.info())\n",
    "annotator2_pre_tbip_speeches['Polarization'] = annotator2_pre_tbip_speeches['Polarization'].fillna(0.0)\n",
    "print(annotator2_pre_tbip_speeches.info())\n",
    "annotator2_pre_tbip_tweets['Polarization'] = annotator2_pre_tbip_tweets['Polarization'].fillna(0.0)\n",
    "print(annotator2_pre_tbip_tweets.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ff14ce87-b33e-483f-8a0d-e20c389e59fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Speeches - Polarization\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.37027707808564225"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Speeches - Polarization')\n",
    "cohen_kappa_score(list(annotator1_pre_tbip_speeches['Polarization']),\n",
    "                  list(annotator2_pre_tbip_speeches['Polarization']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "bc2d7fe0-b348-4d36-bbd2-3dec637d2a67",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tweets - Polarization\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.22062350119904073"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Tweets - Polarization')\n",
    "cohen_kappa_score(list(annotator1_pre_tbip_tweets['Polarization']),\n",
    "                  list(annotator2_pre_tbip_tweets['Polarization']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "ee599dad-35a4-4fa1-ae5b-5043a7987259",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_topic_to_annotator_to_ratings(df1, df2):\n",
    "    l1_topics = list(df1['Topic'])\n",
    "    l2_topics = list(df2['Topic'])\n",
    "    assert l1_topics == l2_topics\n",
    "    \n",
    "    l1_c = list(df1['Coherence'])\n",
    "    l2_c = list(df2['Coherence'])\n",
    "    \n",
    "    l1_p = list(df1['Polarization'])\n",
    "    l2_p = list(df2['Polarization'])\n",
    "    \n",
    "    out = {}\n",
    "    for t, c1, c2, p1, p2 in zip(l1_topics, l1_c, l2_c, l1_p, l2_p):\n",
    "        out[t] = {'annotator1':{'Coherence':c1, 'Polarization':p1}, \n",
    "                  'annotator2':{'Coherence':c2, 'Polarization':p2}}\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "75e1c849-7fbe-4a9e-8572-4c40e220c589",
   "metadata": {},
   "outputs": [],
   "source": [
    "speech_topic_to_annotator_to_ratings = get_topic_to_annotator_to_ratings(annotator1_pre_tbip_speeches,\n",
    "                                                                         annotator2_pre_tbip_speeches)\n",
    "\n",
    "tweet_topic_to_annotator_to_ratings = get_topic_to_annotator_to_ratings(annotator1_pre_tbip_tweets,\n",
    "                                                                         annotator2_pre_tbip_tweets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "b179819f-c86d-4b96-a4bf-7ede48578189",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dataframe_from_annotated_xlsx_file_path(path):\n",
    "    df = pd.read_excel(path,\n",
    "                       sheet_name=None,\n",
    "                       engine='openpyxl')\n",
    "    df = df['Sheet1']\n",
    "    df = df[~pd.isnull(df['Issue'])]\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "d01c456b-62c8-45c7-a2fc-64865c2fa847",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 42 entries, 1 to 944\n",
      "Data columns (total 12 columns):\n",
      " #   Column                                Non-Null Count  Dtype  \n",
      "---  ------                                --------------  -----  \n",
      " 0   Unnamed: 0                            0 non-null      object \n",
      " 1   Unnamed: 1                            0 non-null      float64\n",
      " 2   Issue                                 42 non-null     object \n",
      " 3   Unnamed: 3                            0 non-null      object \n",
      " 4   Unnamed: 4                            0 non-null      float64\n",
      " 5   Unnamed: 5                            0 non-null      float64\n",
      " 6   Label Applicability for a)            42 non-null     object \n",
      " 7   Label Applicability for b)            42 non-null     object \n",
      " 8   Ideological Polarization              42 non-null     object \n",
      " 9   Ideological Position expressed in a)  32 non-null     object \n",
      " 10  Ideological Position expressed in b)  32 non-null     object \n",
      " 11  Notes/Comments                        2 non-null      object \n",
      "dtypes: float64(3), object(9)\n",
      "memory usage: 4.3+ KB\n"
     ]
    }
   ],
   "source": [
    "annotator1_post_tbip_speeches = get_dataframe_from_annotated_xlsx_file_path('venue_diff_polsci/post_tbip_annotation_results/annotation_file_speeches_fg.xlsx')\n",
    "annotator1_post_tbip_speeches.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "d20852e8-6ee2-424c-ba56-31dd8e7a42d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 42 entries, 1 to 944\n",
      "Data columns (total 12 columns):\n",
      " #   Column                                Non-Null Count  Dtype  \n",
      "---  ------                                --------------  -----  \n",
      " 0   Unnamed: 0                            0 non-null      object \n",
      " 1   Unnamed: 1                            0 non-null      float64\n",
      " 2   Issue                                 42 non-null     object \n",
      " 3   Unnamed: 3                            0 non-null      object \n",
      " 4   Unnamed: 4                            0 non-null      float64\n",
      " 5   Unnamed: 5                            0 non-null      float64\n",
      " 6   Label Applicability for a)            42 non-null     object \n",
      " 7   Label Applicability for b)            42 non-null     object \n",
      " 8   Ideological Polarization              42 non-null     object \n",
      " 9   Ideological Position expressed in a)  34 non-null     object \n",
      " 10  Ideological Position expressed in b)  34 non-null     object \n",
      " 11  Notes/Comments                        6 non-null      object \n",
      "dtypes: float64(3), object(9)\n",
      "memory usage: 4.3+ KB\n"
     ]
    }
   ],
   "source": [
    "annotator2_post_tbip_speeches = get_dataframe_from_annotated_xlsx_file_path('venue_diff_polsci/post_tbip_annotation_results/annotation_file_speeches_completed.xlsx')\n",
    "annotator2_post_tbip_speeches.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "cb6b9bd3-08fa-4d3f-87a4-76e66aa168df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 40 entries, 1 to 898\n",
      "Data columns (total 12 columns):\n",
      " #   Column                                Non-Null Count  Dtype  \n",
      "---  ------                                --------------  -----  \n",
      " 0   Unnamed: 0                            0 non-null      object \n",
      " 1   Unnamed: 1                            0 non-null      float64\n",
      " 2   Issue                                 40 non-null     object \n",
      " 3   Unnamed: 3                            0 non-null      object \n",
      " 4   Unnamed: 4                            0 non-null      float64\n",
      " 5   Unnamed: 5                            0 non-null      float64\n",
      " 6   Label Applicability for a)            40 non-null     object \n",
      " 7   Label Applicability for b)            40 non-null     object \n",
      " 8   Ideological Polarization              40 non-null     object \n",
      " 9   Ideological Position expressed in a)  30 non-null     object \n",
      " 10  Ideological Position expressed in b)  30 non-null     object \n",
      " 11  Notes/Comments                        1 non-null      object \n",
      "dtypes: float64(3), object(9)\n",
      "memory usage: 4.1+ KB\n"
     ]
    }
   ],
   "source": [
    "annotator1_post_tbip_tweets = get_dataframe_from_annotated_xlsx_file_path('venue_diff_polsci/post_tbip_annotation_results/annotation_file_tweets_fg.xlsx')\n",
    "annotator1_post_tbip_tweets.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "e5c86cdf-4373-40f9-b027-703e418bc7e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 40 entries, 1 to 898\n",
      "Data columns (total 12 columns):\n",
      " #   Column                                Non-Null Count  Dtype  \n",
      "---  ------                                --------------  -----  \n",
      " 0   Unnamed: 0                            0 non-null      object \n",
      " 1   Unnamed: 1                            0 non-null      float64\n",
      " 2   Issue                                 40 non-null     object \n",
      " 3   Unnamed: 3                            0 non-null      object \n",
      " 4   Unnamed: 4                            0 non-null      float64\n",
      " 5   Unnamed: 5                            0 non-null      float64\n",
      " 6   Label Applicability for a)            40 non-null     object \n",
      " 7   Label Applicability for b)            40 non-null     object \n",
      " 8   Ideological Polarization              40 non-null     object \n",
      " 9   Ideological Position expressed in a)  33 non-null     object \n",
      " 10  Ideological Position expressed in b)  33 non-null     object \n",
      " 11  Notes/Comments                        5 non-null      object \n",
      "dtypes: float64(3), object(9)\n",
      "memory usage: 4.1+ KB\n"
     ]
    }
   ],
   "source": [
    "annotator2_post_tbip_tweets = get_dataframe_from_annotated_xlsx_file_path('venue_diff_polsci/post_tbip_annotation_results/annotation_file_tweets_completed.xlsx')\n",
    "annotator2_post_tbip_tweets.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "a7727b56-d4d9-4a1f-ae33-5cf0d727daaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_label_app_stats(df, colname):\n",
    "    n = len(df)\n",
    "    l = list(df[colname])\n",
    "    is_about, might_about, not_about = 0, 0, 0\n",
    "    for x in l:\n",
    "        if 'IS about' in x:\n",
    "            is_about += 1\n",
    "        elif 'MIGHT' in x:\n",
    "            might_about += 1\n",
    "        elif 'IS NOT' in x:\n",
    "            not_about += 1\n",
    "    print('% of Topics that are about the issue wrt ' + colname + ' = ' + str(round(100*(is_about/n), 2)) + '%')\n",
    "    print('% of Topics that might be about the issue wrt ' + colname + ' = ' + str(round(100*(might_about/n), 2)) + '%')\n",
    "    print('% of Topics that are NOT about the issue wrt ' + colname + ' = ' + str(round(100*(not_about/n), 2)) + '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "defb51cc-fdf3-428a-9f95-c852f57a3711",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% of Topics that are about the issue wrt Label Applicability for a) = 90.48%\n",
      "% of Topics that might be about the issue wrt Label Applicability for a) = 7.14%\n",
      "% of Topics that are NOT about the issue wrt Label Applicability for a) = 2.38%\n"
     ]
    }
   ],
   "source": [
    "get_label_app_stats(annotator1_post_tbip_speeches, \n",
    "                    'Label Applicability for a)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "a7ce1778-5566-4856-be7b-e67da698ec53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% of Topics that are about the issue wrt Label Applicability for a) = 83.33%\n",
      "% of Topics that might be about the issue wrt Label Applicability for a) = 14.29%\n",
      "% of Topics that are NOT about the issue wrt Label Applicability for a) = 2.38%\n"
     ]
    }
   ],
   "source": [
    "get_label_app_stats(annotator2_post_tbip_speeches, \n",
    "                    'Label Applicability for a)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "5dd3ce5d-dbcf-4be4-98dd-f36741656a9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Speeches - Label applicability for a)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8057803468208092"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Speeches - Label applicability for a)')\n",
    "cohen_kappa_score(list(annotator1_post_tbip_speeches['Label Applicability for a)']),\n",
    "                  list(annotator2_post_tbip_speeches['Label Applicability for a)']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "bc039206-e37d-4c94-a9b4-9c1bda157df2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% of Topics that are about the issue wrt Label Applicability for a) = 92.5%\n",
      "% of Topics that might be about the issue wrt Label Applicability for a) = 7.5%\n",
      "% of Topics that are NOT about the issue wrt Label Applicability for a) = 0.0%\n"
     ]
    }
   ],
   "source": [
    "get_label_app_stats(annotator1_post_tbip_tweets, \n",
    "                    'Label Applicability for a)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "b232c817-cbcb-40ac-a933-25789250eee4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% of Topics that are about the issue wrt Label Applicability for a) = 57.5%\n",
      "% of Topics that might be about the issue wrt Label Applicability for a) = 40.0%\n",
      "% of Topics that are NOT about the issue wrt Label Applicability for a) = 2.5%\n"
     ]
    }
   ],
   "source": [
    "get_label_app_stats(annotator2_post_tbip_tweets, \n",
    "                    'Label Applicability for a)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "b537e653-6ef6-4d5e-a0e3-bfa95ad0ecbf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tweets - Label applicability for a)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.5939086294416244"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Tweets - Label applicability for a)')\n",
    "cohen_kappa_score(list(annotator1_post_tbip_tweets['Label Applicability for a)']),\n",
    "                  list(annotator2_post_tbip_tweets['Label Applicability for a)']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "a54c3de9-aa59-4fa9-9807-292b00affabe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% of Topics that are about the issue wrt Label Applicability for b) = 90.48%\n",
      "% of Topics that might be about the issue wrt Label Applicability for b) = 9.52%\n",
      "% of Topics that are NOT about the issue wrt Label Applicability for b) = 0.0%\n"
     ]
    }
   ],
   "source": [
    "get_label_app_stats(annotator1_post_tbip_speeches, \n",
    "                    'Label Applicability for b)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "96a0370e-61fe-47e6-b8d4-e0a75b1b4c68",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% of Topics that are about the issue wrt Label Applicability for b) = 80.95%\n",
      "% of Topics that might be about the issue wrt Label Applicability for b) = 14.29%\n",
      "% of Topics that are NOT about the issue wrt Label Applicability for b) = 4.76%\n"
     ]
    }
   ],
   "source": [
    "get_label_app_stats(annotator2_post_tbip_speeches, \n",
    "                    'Label Applicability for b)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "4a2cc769-d022-4370-98cb-ce11e6c0548b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Speeches - Label applicability for b)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8299595141700404"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Speeches - Label applicability for b)')\n",
    "cohen_kappa_score(list(annotator1_post_tbip_speeches['Label Applicability for b)']),\n",
    "                  list(annotator2_post_tbip_speeches['Label Applicability for b)']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "3b00f9a2-5471-4f63-a773-f9a8d7a36671",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% of Topics that are about the issue wrt Label Applicability for b) = 75.0%\n",
      "% of Topics that might be about the issue wrt Label Applicability for b) = 22.5%\n",
      "% of Topics that are NOT about the issue wrt Label Applicability for b) = 2.5%\n"
     ]
    }
   ],
   "source": [
    "get_label_app_stats(annotator1_post_tbip_tweets, \n",
    "                    'Label Applicability for b)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "45deb821-462f-42b6-9382-8bd732cdb88e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% of Topics that are about the issue wrt Label Applicability for b) = 65.0%\n",
      "% of Topics that might be about the issue wrt Label Applicability for b) = 30.0%\n",
      "% of Topics that are NOT about the issue wrt Label Applicability for b) = 5.0%\n"
     ]
    }
   ],
   "source": [
    "get_label_app_stats(annotator2_post_tbip_tweets, \n",
    "                    'Label Applicability for b)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "559a6691-f284-4a23-9719-91751e637a3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tweets - Label applicability for b)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.6190476190476191"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Tweets - Label applicability for b)')\n",
    "cohen_kappa_score(list(annotator1_post_tbip_tweets['Label Applicability for b)']),\n",
    "                  list(annotator2_post_tbip_tweets['Label Applicability for b)']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "d2fb0359-4d8e-4ea8-b919-918818ff1f8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_post_tbip_id_pol_stats(df):\n",
    "    l_a = list(df['Label Applicability for a)'])\n",
    "    l_b = list(df['Label Applicability for b)'])\n",
    "    \n",
    "    l_i = list(df['Ideological Polarization'])\n",
    "    \n",
    "    n = 0.0\n",
    "    do_not, somewhat, do, unsure = 0, 0, 0, 0\n",
    "    for a, b, i in zip(l_a, l_b, l_i):\n",
    "        if 'IS NOT' in a or 'IS NOT' in b: #consider only cases where label at least might be applicable for both a) and b)\n",
    "            continue\n",
    "        n += 1.0\n",
    "        if 'DO NOT' in i:\n",
    "            do_not += 1\n",
    "        elif 'SOMEWHAT' in i:\n",
    "            somewhat += 1\n",
    "        elif 'Unsure' in i:\n",
    "            unsure += 1\n",
    "        else:\n",
    "            do += 1\n",
    "    print('% of Topics for which a and b do represent polarized perspectives = ' + str(round(100*(do/n), 2)) + '%')\n",
    "    print('% of Topics for which a and b represent SOMEWHAT polarized perspectives = ' + str(round(100*(somewhat/n), 2)) + '%')\n",
    "    print('% of Topics for which a and b DO NOT represent polarized perspectives = ' + str(round(100*(do_not/n), 2)) + '%')\n",
    "    print('% of Topics UNSURE = ' + str(round(100*(unsure/n), 2)) + '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "345fe61f-e95c-49c7-ab4e-223fd3fd64fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% of Topics for which a and b do represent polarized perspectives = 31.71%\n",
      "% of Topics for which a and b represent SOMEWHAT polarized perspectives = 41.46%\n",
      "% of Topics for which a and b DO NOT represent polarized perspectives = 24.39%\n",
      "% of Topics UNSURE = 2.44%\n"
     ]
    }
   ],
   "source": [
    "get_post_tbip_id_pol_stats(annotator1_post_tbip_speeches)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "9903a476-19d0-424e-852e-740af946a79b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% of Topics for which a and b do represent polarized perspectives = 35.9%\n",
      "% of Topics for which a and b represent SOMEWHAT polarized perspectives = 30.77%\n",
      "% of Topics for which a and b DO NOT represent polarized perspectives = 17.95%\n",
      "% of Topics UNSURE = 15.38%\n"
     ]
    }
   ],
   "source": [
    "get_post_tbip_id_pol_stats(annotator2_post_tbip_speeches)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "c57e7fd2-b741-412b-b1c9-6b0bdde75423",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Speeches - Ideological Polarization\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.33009708737864063"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Speeches - Ideological Polarization')\n",
    "cohen_kappa_score(list(annotator1_post_tbip_speeches['Ideological Polarization']),\n",
    "                  list(annotator2_post_tbip_speeches['Ideological Polarization']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "f4db8640-3429-4ba7-b58b-a91e242e1080",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% of Topics for which a and b do represent polarized perspectives = 28.21%\n",
      "% of Topics for which a and b represent SOMEWHAT polarized perspectives = 28.21%\n",
      "% of Topics for which a and b DO NOT represent polarized perspectives = 43.59%\n",
      "% of Topics UNSURE = 0.0%\n"
     ]
    }
   ],
   "source": [
    "get_post_tbip_id_pol_stats(annotator1_post_tbip_tweets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "294345dd-445b-47f1-bae6-30a25fd6e58e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% of Topics for which a and b do represent polarized perspectives = 42.11%\n",
      "% of Topics for which a and b represent SOMEWHAT polarized perspectives = 31.58%\n",
      "% of Topics for which a and b DO NOT represent polarized perspectives = 15.79%\n",
      "% of Topics UNSURE = 10.53%\n"
     ]
    }
   ],
   "source": [
    "get_post_tbip_id_pol_stats(annotator2_post_tbip_tweets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "de66b801-d3f6-46ef-b4e4-72c734fd561d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tweets - Ideological Polarization\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.43494423791821557"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Tweets - Ideological Polarization')\n",
    "cohen_kappa_score(list(annotator1_post_tbip_tweets['Ideological Polarization']),\n",
    "                  list(annotator2_post_tbip_tweets['Ideological Polarization']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4501690f-2837-4117-bfe6-3c9b488f0883",
   "metadata": {},
   "source": [
    "for the topics pre-tbip expected to have political polarization by either annotator, how many were deemed ideologically polarized post-tbip by either annotator (%)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "9516225d-7aa9-4626-813f-42b6ec579740",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_topic_nums_expected_to_be_polarized_by_either_annotator(df1, df2):\n",
    "    out = []\n",
    "    df1 = df1[~pd.isnull(df1['Polarization'])]\n",
    "    df2 = df2[~pd.isnull(df2['Polarization'])]\n",
    "    topics1, p1 = list(df1['Topic']), list(df1['Polarization'])\n",
    "    topics2, p2 = list(df2['Topic']), list(df2['Polarization'])\n",
    "    for t, p in zip(topics1, p1):\n",
    "        if p==2.0 or p==3.0:\n",
    "            out.append(t.split()[1])\n",
    "        else:\n",
    "            if t in topics2:\n",
    "                i2 = topics2.index(t)\n",
    "                if p2[i2]==2.0 or p2[i2]==3.0:\n",
    "                    out.append(t.split()[1])\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "a995433d-c626-443b-a2e7-3c64dd8999c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28\n"
     ]
    }
   ],
   "source": [
    "speeches_topic_nums_expected_to_be_polarized_by_either_annotator = get_topic_nums_expected_to_be_polarized_by_either_annotator(annotator1_pre_tbip_speeches,\n",
    "                                                                                                                               annotator2_pre_tbip_speeches)\n",
    "print(len(speeches_topic_nums_expected_to_be_polarized_by_either_annotator))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "8a94cd07-3538-4d65-9eb2-2179206d0af4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28\n"
     ]
    }
   ],
   "source": [
    "tweets_topic_nums_expected_to_be_polarized_by_either_annotator = get_topic_nums_expected_to_be_polarized_by_either_annotator(annotator1_pre_tbip_tweets,\n",
    "                                                                                                                             annotator2_pre_tbip_tweets)\n",
    "print(len(tweets_topic_nums_expected_to_be_polarized_by_either_annotator))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "5927540b-d210-4dc1-9e7e-450c6a502263",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_post_tbip_polarized_topic_nums_by_either_annotator(df1, df2):\n",
    "    \n",
    "    out = set()\n",
    "    \n",
    "    l_topics = list(df1['Issue'])\n",
    "    l_a = list(df1['Label Applicability for a)'])\n",
    "    l_b = list(df1['Label Applicability for b)'])\n",
    "    \n",
    "    l_i = list(df1['Ideological Polarization'])\n",
    "    \n",
    "    for tn, a, b, i in zip(l_topics, l_a, l_b, l_i):\n",
    "        if 'IS NOT' in a or 'IS NOT' in b: #consider only cases where label at least might be applicable for both a) and b)\n",
    "            continue\n",
    "        if 'DO NOT' in i:\n",
    "            continue\n",
    "        elif 'SOMEWHAT' in i:\n",
    "            out.add(tn.split()[1])\n",
    "        elif 'Unsure' in i:\n",
    "            continue\n",
    "        else:\n",
    "            out.add(tn.split()[1])\n",
    "    \n",
    "    l_topics = list(df2['Issue'])\n",
    "    l_a = list(df2['Label Applicability for a)'])\n",
    "    l_b = list(df2['Label Applicability for b)'])\n",
    "    \n",
    "    l_i = list(df2['Ideological Polarization'])\n",
    "    \n",
    "    for tn, a, b, i in zip(l_topics, l_a, l_b, l_i):\n",
    "        if 'IS NOT' in a or 'IS NOT' in b: #consider only cases where label at least might be applicable for both a) and b)\n",
    "            continue\n",
    "        if 'DO NOT' in i:\n",
    "            continue\n",
    "        elif 'SOMEWHAT' in i:\n",
    "            out.add(tn.split()[1])\n",
    "        elif 'Unsure' in i:\n",
    "            continue\n",
    "        else:\n",
    "            out.add(tn.split()[1])\n",
    "            \n",
    "    return list(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "67addd58-5f20-4739-b394-e2161255449f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34\n"
     ]
    }
   ],
   "source": [
    "speeches_topic_nums_polarized_per_either_annotator_post_tbip = get_post_tbip_polarized_topic_nums_by_either_annotator(annotator1_post_tbip_speeches,\n",
    "                                                                                                                      annotator2_post_tbip_speeches)\n",
    "print(len(speeches_topic_nums_polarized_per_either_annotator_post_tbip))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "361de9ab-34bc-459d-94f9-6bb950cb6705",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30\n"
     ]
    }
   ],
   "source": [
    "tweets_topic_nums_polarized_per_either_annotator_post_tbip = get_post_tbip_polarized_topic_nums_by_either_annotator(annotator1_post_tbip_tweets,\n",
    "                                                                                                                    annotator2_post_tbip_tweets)\n",
    "print(len(tweets_topic_nums_polarized_per_either_annotator_post_tbip))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "adaf6ada-d381-475f-afc4-b94a2b310bf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "issues_rated_post_tbip_for_speeches = list(annotator1_post_tbip_speeches['Issue'])\n",
    "issues_rated_post_tbip_for_speeches = list(map(lambda x:x.split()[1], issues_rated_post_tbip_for_speeches))\n",
    "issues_rated_post_tbip_for_tweets = list(annotator1_post_tbip_tweets['Issue'])\n",
    "issues_rated_post_tbip_for_tweets = list(map(lambda x:x.split()[1], issues_rated_post_tbip_for_tweets))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "eb9b4881-fcbd-4863-9968-1355d39eb8da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For Floor Speeches - \n",
      "81.82% of pre-tbip expected to have political polarization by either annotator were deemed ideologically polarized post-tbip by either annotator\n"
     ]
    }
   ],
   "source": [
    "print('For Floor Speeches - ')\n",
    "speeches_topic_nums_expected_to_be_polarized_by_either_annotator = list(set(speeches_topic_nums_expected_to_be_polarized_by_either_annotator).intersection(list(issues_rated_post_tbip_for_speeches)))\n",
    "n = len(speeches_topic_nums_expected_to_be_polarized_by_either_annotator)\n",
    "c = 0.0\n",
    "for tn in speeches_topic_nums_expected_to_be_polarized_by_either_annotator:\n",
    "    if tn in speeches_topic_nums_polarized_per_either_annotator_post_tbip:\n",
    "        c += 1.0\n",
    "print(str(round(100*(c/n), 2)) + '% of pre-tbip expected to have political polarization by either annotator were deemed ideologically polarized post-tbip by either annotator')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "bc353b25-45fc-48bf-8ec9-6b832887cadc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For Twitter - \n",
      "72.73% of pre-tbip expected to have political polarization by either annotator were deemed ideologically polarized post-tbip by either annotator\n"
     ]
    }
   ],
   "source": [
    "print('For Twitter - ')\n",
    "tweets_topic_nums_expected_to_be_polarized_by_either_annotator = list(set(tweets_topic_nums_expected_to_be_polarized_by_either_annotator).intersection(list(issues_rated_post_tbip_for_tweets)))\n",
    "n = len(tweets_topic_nums_expected_to_be_polarized_by_either_annotator)\n",
    "c = 0.0\n",
    "for tn in tweets_topic_nums_expected_to_be_polarized_by_either_annotator:\n",
    "    if tn in tweets_topic_nums_polarized_per_either_annotator_post_tbip:\n",
    "        c += 1.0\n",
    "print(str(round(100*(c/n), 2)) + '% of pre-tbip expected to have political polarization by either annotator were deemed ideologically polarized post-tbip by either annotator')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a8ec98c-8ec9-4a26-a678-311aa59dbf6d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "f6c1eb59-92d4-4446-ba63-5ad2f08b8d6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "topic_ind_to_a_b_info_speech = pickle.load(open('venue_diff_polsci/floor_speeches/topic_ind_to_a_b_info_post_tbip.pkl', \n",
    "                                                'rb'))\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "62d30ea6-802d-48f5-a7b0-5a17be1dd6fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "topic_ind_to_a_b_info_tweet = pickle.load(open('venue_diff_polsci/twitter/topic_ind_to_a_b_info_post_tbip.pkl', \n",
    "                                                'rb'))\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "9301031c-da9b-446e-87d2-b248f8c8de8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_post_tbip_topic_ind_to_id_rating(df):\n",
    "    df = df[~pd.isnull(df['Ideological Position expressed in a)'])]\n",
    "    issues, id_a, id_b = list(df['Issue']), list(df['Ideological Position expressed in a)']), list(df['Ideological Position expressed in b)'])\n",
    "    out = {}\n",
    "    for t, rating_a, rating_b in zip(issues, id_a, id_b):\n",
    "        topic_ind = int(t.split()[1]) - 1\n",
    "        out[topic_ind] = {'a': rating_a, \n",
    "                          'b': rating_b}\n",
    "        \n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "cf23a3e5-3850-4bf9-a78b-82df560c2c8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32\n"
     ]
    }
   ],
   "source": [
    "post_tbip_topic_ind_to_a_b_rating_speech_ann1 = get_post_tbip_topic_ind_to_id_rating(annotator1_post_tbip_speeches)\n",
    "print(len(post_tbip_topic_ind_to_a_b_rating_speech_ann1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "16584790-7dbc-4920-8bb5-2fb40ff5850e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34\n"
     ]
    }
   ],
   "source": [
    "post_tbip_topic_ind_to_a_b_rating_speech_ann2 = get_post_tbip_topic_ind_to_id_rating(annotator2_post_tbip_speeches)\n",
    "print(len(post_tbip_topic_ind_to_a_b_rating_speech_ann2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "b036346c-123e-4d99-9495-50dffe815967",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30\n"
     ]
    }
   ],
   "source": [
    "post_tbip_topic_ind_to_a_b_rating_tweet_ann1 = get_post_tbip_topic_ind_to_id_rating(annotator1_post_tbip_tweets)\n",
    "print(len(post_tbip_topic_ind_to_a_b_rating_tweet_ann1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "dd7d9689-b9c1-4b62-83f5-67bb46714f46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33\n"
     ]
    }
   ],
   "source": [
    "post_tbip_topic_ind_to_a_b_rating_tweet_ann2 = get_post_tbip_topic_ind_to_id_rating(annotator2_post_tbip_tweets)\n",
    "print(len(post_tbip_topic_ind_to_a_b_rating_tweet_ann2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "0c25b084-88fe-441f-b268-967952ff1922",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Annotator 1, Speeches\n",
      "\n",
      "Liberal per TBIP - \n",
      "{'Conservative': 0, 'Liberal': 29, 'Unsure': 3}\n",
      "\n",
      "Conservative per TBIP - \n",
      "{'Conservative': 25, 'Liberal': 0, 'Unsure': 7}\n"
     ]
    }
   ],
   "source": [
    "liberal_tbip_speeches_breakdown1 = {'Conservative': 0,\n",
    "                                    'Liberal': 0,\n",
    "                                    'Unsure': 0}\n",
    "conservative_tbip_speeches_breakdown1 = {'Conservative': 0,\n",
    "                                         'Liberal': 0,\n",
    "                                         'Unsure': 0}\n",
    "#c = 0\n",
    "on_issue = 0\n",
    "for ti in topic_ind_to_a_b_info_speech:\n",
    "    if on_issue in post_tbip_topic_ind_to_a_b_rating_speech_ann1:\n",
    "        #c += 1\n",
    "        if topic_ind_to_a_b_info_speech[ti]['a'] == -1:\n",
    "            liberal_tbip_speeches_breakdown1[post_tbip_topic_ind_to_a_b_rating_speech_ann1[on_issue]['a']] += 1\n",
    "            conservative_tbip_speeches_breakdown1[post_tbip_topic_ind_to_a_b_rating_speech_ann1[on_issue]['b']] += 1\n",
    "        else:\n",
    "            liberal_tbip_speeches_breakdown1[post_tbip_topic_ind_to_a_b_rating_speech_ann1[on_issue]['b']] += 1\n",
    "            conservative_tbip_speeches_breakdown1[post_tbip_topic_ind_to_a_b_rating_speech_ann1[on_issue]['a']] += 1\n",
    "    on_issue += 1\n",
    "#print(c)\n",
    "print('Annotator 1, Speeches')\n",
    "print('')\n",
    "print('Liberal per TBIP - ')\n",
    "print(liberal_tbip_speeches_breakdown1)\n",
    "print('')\n",
    "print('Conservative per TBIP - ')\n",
    "print(conservative_tbip_speeches_breakdown1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "a13223f6-7b6f-4573-ae3a-64e034592475",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Annotator 2, Speeches\n",
      "\n",
      "Liberal per TBIP - \n",
      "{'Conservative': 3, 'Liberal': 23, 'Unsure': 8}\n",
      "\n",
      "Conservative per TBIP - \n",
      "{'Conservative': 23, 'Liberal': 3, 'Unsure': 8}\n"
     ]
    }
   ],
   "source": [
    "liberal_tbip_speeches_breakdown2 = {'Conservative': 0,\n",
    "                                    'Liberal': 0,\n",
    "                                    'Unsure': 0}\n",
    "conservative_tbip_speeches_breakdown2 = {'Conservative': 0,\n",
    "                                         'Liberal': 0,\n",
    "                                         'Unsure': 0}\n",
    "#c = 0\n",
    "on_issue = 0\n",
    "for ti in topic_ind_to_a_b_info_speech:\n",
    "    if on_issue in post_tbip_topic_ind_to_a_b_rating_speech_ann2:\n",
    "        #c += 1\n",
    "        if topic_ind_to_a_b_info_speech[ti]['a'] == -1:\n",
    "            liberal_tbip_speeches_breakdown2[post_tbip_topic_ind_to_a_b_rating_speech_ann2[on_issue]['a']] += 1\n",
    "            conservative_tbip_speeches_breakdown2[post_tbip_topic_ind_to_a_b_rating_speech_ann2[on_issue]['b']] += 1\n",
    "        else:\n",
    "            liberal_tbip_speeches_breakdown2[post_tbip_topic_ind_to_a_b_rating_speech_ann2[on_issue]['b']] += 1\n",
    "            conservative_tbip_speeches_breakdown2[post_tbip_topic_ind_to_a_b_rating_speech_ann2[on_issue]['a']] += 1\n",
    "    on_issue += 1\n",
    "#print(c)\n",
    "print('Annotator 2, Speeches')\n",
    "print('')\n",
    "print('Liberal per TBIP - ')\n",
    "print(liberal_tbip_speeches_breakdown2)\n",
    "print('')\n",
    "print('Conservative per TBIP - ')\n",
    "print(conservative_tbip_speeches_breakdown2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "caa629b2-ba56-4ca6-b0dc-a62d473aadfd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Annotator 1, Tweets\n",
      "\n",
      "Liberal per TBIP - \n",
      "{'Conservative': 0, 'Liberal': 29, 'Unsure': 1}\n",
      "\n",
      "Conservative per TBIP - \n",
      "{'Conservative': 23, 'Liberal': 2, 'Unsure': 5}\n"
     ]
    }
   ],
   "source": [
    "liberal_tbip_tweets_breakdown1 = {'Conservative': 0,\n",
    "                                    'Liberal': 0,\n",
    "                                    'Unsure': 0}\n",
    "conservative_tbip_tweets_breakdown1 = {'Conservative': 0,\n",
    "                                         'Liberal': 0,\n",
    "                                         'Unsure': 0}\n",
    "#c = 0\n",
    "on_issue = 0\n",
    "for ti in topic_ind_to_a_b_info_tweet:\n",
    "    if on_issue in post_tbip_topic_ind_to_a_b_rating_tweet_ann1:\n",
    "        #c += 1\n",
    "        if topic_ind_to_a_b_info_tweet[ti]['a'] == 1:\n",
    "            liberal_tbip_tweets_breakdown1[post_tbip_topic_ind_to_a_b_rating_tweet_ann1[on_issue]['a']] += 1\n",
    "            conservative_tbip_tweets_breakdown1[post_tbip_topic_ind_to_a_b_rating_tweet_ann1[on_issue]['b']] += 1\n",
    "        else:\n",
    "            liberal_tbip_tweets_breakdown1[post_tbip_topic_ind_to_a_b_rating_tweet_ann1[on_issue]['b']] += 1\n",
    "            conservative_tbip_tweets_breakdown1[post_tbip_topic_ind_to_a_b_rating_tweet_ann1[on_issue]['a']] += 1\n",
    "    on_issue += 1\n",
    "#print(c)\n",
    "print('Annotator 1, Tweets')\n",
    "print('')\n",
    "print('Liberal per TBIP - ')\n",
    "print(liberal_tbip_tweets_breakdown1)\n",
    "print('')\n",
    "print('Conservative per TBIP - ')\n",
    "print(conservative_tbip_tweets_breakdown1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "35d26bd9-9617-4ddd-93de-67c5371b2d2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Annotator 2, Tweets\n",
      "\n",
      "Liberal per TBIP - \n",
      "{'Conservative': 0, 'Liberal': 28, 'Unsure': 5}\n",
      "\n",
      "Conservative per TBIP - \n",
      "{'Conservative': 28, 'Liberal': 0, 'Unsure': 5}\n"
     ]
    }
   ],
   "source": [
    "liberal_tbip_tweets_breakdown2 = {'Conservative': 0,\n",
    "                                    'Liberal': 0,\n",
    "                                    'Unsure': 0}\n",
    "conservative_tbip_tweets_breakdown2 = {'Conservative': 0,\n",
    "                                         'Liberal': 0,\n",
    "                                         'Unsure': 0}\n",
    "#c = 0\n",
    "on_issue = 0\n",
    "for ti in topic_ind_to_a_b_info_tweet:\n",
    "    if on_issue in post_tbip_topic_ind_to_a_b_rating_tweet_ann2:\n",
    "        #c += 1\n",
    "        if topic_ind_to_a_b_info_tweet[ti]['a'] == 1:\n",
    "            liberal_tbip_tweets_breakdown2[post_tbip_topic_ind_to_a_b_rating_tweet_ann2[on_issue]['a']] += 1\n",
    "            conservative_tbip_tweets_breakdown2[post_tbip_topic_ind_to_a_b_rating_tweet_ann2[on_issue]['b']] += 1\n",
    "        else:\n",
    "            liberal_tbip_tweets_breakdown2[post_tbip_topic_ind_to_a_b_rating_tweet_ann2[on_issue]['b']] += 1\n",
    "            conservative_tbip_tweets_breakdown2[post_tbip_topic_ind_to_a_b_rating_tweet_ann2[on_issue]['a']] += 1\n",
    "    on_issue += 1\n",
    "#print(c)\n",
    "print('Annotator 2, Tweets')\n",
    "print('')\n",
    "print('Liberal per TBIP - ')\n",
    "print(liberal_tbip_tweets_breakdown2)\n",
    "print('')\n",
    "print('Conservative per TBIP - ')\n",
    "print(conservative_tbip_tweets_breakdown2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "211f79d5-7528-41ef-84f7-0df0c2b50b88",
   "metadata": {},
   "source": [
    "for issues rated as liberal/conservative/unsure -- how many were accurate for both annotators?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "c5b4310a-16b3-434e-8633-64b929c982f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.90625"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "29/32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "e694e051-7563-4919-a391-85c623e3df62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.78125"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "25/32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "834375d3-3bd8-4f53-83b1-cc365f4b0019",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6764705882352942"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "23/34"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "d2d56f05-d5cb-43e4-aed7-22866a8f4ec1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08823529411764706"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3/34"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "e20b7512-a478-4915-abad-31eb9e1449cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9666666666666667"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "29/30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "f02ebc72-e254-4a13-8189-b2afd901939d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7666666666666667"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "23/30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "393ae21e-8de4-4fec-9e91-d0229f5bc9e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06666666666666667"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2/30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "9588aa49-ec71-4d42-8a6b-ff8590a7bf61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8484848484848485"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "28/33"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "977af4ce-14e6-452e-b3b9-0e6543efaeb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Issues applicable = 27\n",
      "Speeches, perspective a\n",
      "0.5324675324675325\n",
      "\n",
      "Speeches, perspective b\n",
      "0.6917808219178082\n"
     ]
    }
   ],
   "source": [
    "speeches1 = annotator1_post_tbip_speeches[~pd.isnull(annotator1_post_tbip_speeches['Ideological Position expressed in a)'])]\n",
    "speeches2 = annotator2_post_tbip_speeches[~pd.isnull(annotator2_post_tbip_speeches['Ideological Position expressed in a)'])]\n",
    "issues_ap_to_both = list(set(speeches1['Issue']).intersection(speeches2['Issue']))\n",
    "print('Issues applicable = ' + str(len(issues_ap_to_both)))\n",
    "speeches1 = speeches1[speeches1['Issue'].isin(issues_ap_to_both)]\n",
    "speeches2 = speeches2[speeches2['Issue'].isin(issues_ap_to_both)]\n",
    "print('Speeches, perspective a')\n",
    "print(cohen_kappa_score(list(speeches1['Ideological Position expressed in a)']),\n",
    "                  list(speeches2['Ideological Position expressed in a)'])))\n",
    "print('')\n",
    "print('Speeches, perspective b')\n",
    "print(cohen_kappa_score(list(speeches1['Ideological Position expressed in b)']),\n",
    "                  list(speeches2['Ideological Position expressed in b)'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "28d75b28-2941-464d-b32d-bfbbf66babe0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Issues applicable = 28\n",
      "Tweets, perspective a\n",
      "0.7370892018779343\n",
      "\n",
      "Tweets, perspective b\n",
      "0.6207674943566591\n"
     ]
    }
   ],
   "source": [
    "tweets1 = annotator1_post_tbip_tweets[~pd.isnull(annotator1_post_tbip_tweets['Ideological Position expressed in a)'])]\n",
    "tweets2 = annotator2_post_tbip_tweets[~pd.isnull(annotator2_post_tbip_tweets['Ideological Position expressed in a)'])]\n",
    "issues_ap_to_both = list(set(tweets1['Issue']).intersection(tweets2['Issue']))\n",
    "print('Issues applicable = ' + str(len(issues_ap_to_both)))\n",
    "tweets1 = tweets1[tweets1['Issue'].isin(issues_ap_to_both)]\n",
    "tweets2 = tweets2[tweets2['Issue'].isin(issues_ap_to_both)]\n",
    "print('Tweets, perspective a')\n",
    "print(cohen_kappa_score(list(tweets1['Ideological Position expressed in a)']),\n",
    "                  list(tweets2['Ideological Position expressed in a)'])))\n",
    "print('')\n",
    "print('Tweets, perspective b')\n",
    "print(cohen_kappa_score(list(tweets1['Ideological Position expressed in b)']),\n",
    "                  list(tweets2['Ideological Position expressed in b)'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe0b8a51-bc51-4482-b76a-8d5e64757ef6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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